code: def solve_interpolation(
      train_points, train_values, order, regularization_weight
  ):
      device = train_points.device
      b, n, d = train_points.shape
      k = train_values.shape[-1]
  
      c = train_points
      f = train_values.float()
  
      matrix_a = phi(
          cross_squared_distance_matrix(c, c), order
      ).unsqueeze(
          0
      )  # [b, n, n]
  
      # Append ones to the feature values for the bias term in the linear model.
      ones = torch.ones(
          1, dtype=train_points.dtype, device=device
      ).view([-1, 1, 1])
      matrix_b = torch.cat((c, ones), 2).float()  # [b, n, d + 1]
  
      # [b, n + d + 1, n]
      left_block = torch.cat(
          (matrix_a, torch.transpose(matrix_b, 2, 1)), 1
      )
  
      num_b_cols = matrix_b.shape[2]  # d + 1
  
      # In Tensorflow, zeros are used here. Pytorch solve fails with zeros
      # for some reason we don't understand.
      # So instead we use very tiny randn values (variance of one, zero mean)
      # on one side of our multiplication.
      lhs_zeros = (
          torch.randn((b, num_b_cols, num_b_cols), device=device) / 1e10
      )
      right_block = torch.cat(
          (matrix_b, lhs_zeros), 1
      )  # [b, n + d + 1, d + 1]
      lhs = torch.cat(
          (left_block, right_block), 2
      )  # [b, n + d + 1, n + d + 1]
  
      rhs_zeros = torch.zeros(
          (b, d + 1, k), dtype=train_points.dtype, device=device
      ).float()
      rhs = torch.cat((f, rhs_zeros), 1)  # [b, n + d + 1, k]
  
      # Then, solve the linear system and unpack the results.
      X, LU = torch.gesv(rhs, lhs)
      w = X[:, :n, :]
      v = X[:, n:, :]
  
      return w, v
  
file_path: espnet/espnet/utils/spec_augment.py
imports: import random
  import torch
label_desc: Use image node_id 11 for calling a global function with example usage: solve_interpolation(train_points, train_values, order, regularization_weight) and returns: w,  v